KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an r -band 5 σ magnitude limit of 24.8 and survey footprint of 1347 deg 2 , it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the 27 σ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests.

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Publication Details

Journal
Astronomy and Astrophysics
Published
2025-01-22
DOI
https://doi.org/10.1051/0004-6361/202452808
Citations
6
Primary Topic
Astronomy and Astrophysical Research
Type
article
Field-Weighted Citation Impact
7.15

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article

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Shangrong Li, Ziang Yan, Giorgio Francesco Lesci, Priyanka Jalan et al.
6 citations
Astronomy and Astrophysics
Astronomy and Astrophysical Research
7.15
article

KiDS-Legacy: Angular galaxy clustering from deep surveys with complex selection effects

Shangrong Li, Ziang Yan, Giorgio Francesco Lesci, Priyanka Jalan, Catherine Heymans, Constance Mahony, L. Moscardini, Mijin Yoon, Maciej Bilicki, Benjamin Joachimi, Shahab Joudaki, H. Hildebrandt, Andrej Dvornik, Maximilian von Wietersheim-Kramsta, Christos Georgiou, Nora Elisa Chisari, R. Nicola Napolitano, Robert Reischke, Laila Linke, Marika Asgari, Arthur Loureiro, Benjamin Stölzner, Helen Wright
article en
6 citations

Abstract

Photometric galaxy surveys, despite their limited resolution along the line of sight, encode rich information about the large-scale structure (LSS) of the Universe thanks to the high number density and extensive depth of the data. However, the complicated selection effects in wide and deep surveys can potentially cause significant bias in the angular two-point correlation function (2PCF) measured from those surveys. In this paper, we measure the 2PCF from the newly published KiDS-Legacy sample. Given an r -band 5 σ magnitude limit of 24.8 and survey footprint of 1347 deg 2 , it achieves an excellent combination of sky coverage and depth for such a measurement. We find that complex selection effects, primarily induced by varying seeing, introduce over-estimation of the 2PCF by approximately an order of magnitude. To correct for such effects, we apply a machine learning-based method to recover an organised random (OR) that presents the same selection pattern as the galaxy sample. The basic idea is to find the selection-induced clustering of galaxies using a combination of self-organising maps (SOMs) and hierarchical clustering (HC). This unsupervised machine learning method is able to recover complicated selection effects without specifying their functional forms. We validate this SOM+HC method on mock deep galaxy samples with realistic systematics and selections derived from the KiDS-Legacy catalogue. Using mock data, we demonstrate that the OR delivers unbiased 2PCF cosmological parameter constraints, removing the 27 σ offset in the galaxy bias parameter that is recovered when adopting uniform randoms. Blinded measurements on the real KiDS-Legacy data show that the corrected 2PCF is robust to the SOM+HC configuration near the optimal set-up suggested by the mock tests.

Astronomy and AstrophysicsVol. 694
Leiden University (NL), Centre National de la Recherche Scientifique (FR), University of Bonn (DE), Stockholm University (SE), Durham University (GB), Universität Innsbruck (AT), Utrecht University (NL), Aix-Marseille Université (FR), Center for Theoretical Physics (PL), University of Oxford (GB), Royal Observatory (GB), AlbaNova (SE), Istituto Nazionale di Fisica Nucleare, Sezione di Bologna (IT), Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (ES), Osservatorio di Astrofisica e Scienza dello Spazio, University College London (GB), Imperial College London (GB), Donostia International Physics Center (ES), University of Portsmouth (GB), University of Naples Federico II (IT), Newcastle University (GB), Ruhr University Bochum (DE), University of Bologna (IT), University of Edinburgh (GB), Polish Academy of Sciences (PL)
Science and Technology Facilities Council
Openalex Percentile: Top 4%
Astronomy and Astrophysical Research
7.15
DOIOpen Access PDFOpenAlex
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